The Digital Twin as a Mediator for the Digitalization and Conservation of Expert Knowledge

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The Digital Twin as a Mediator for the Digitalization and Conservation of Expert Knowledge

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Hüsener, Dominik / Schluse, Michael / Kaufmann, Dorit / Roßmann, Jürgen

A Digital Twin is a virtual representation of a physical asset. It reflects the current state of that machine through a model and the data as observed by sensors in the real machine; and enables effective and efficient interaction with the machine, i.e. for monitoring and control purposes. The Digital Twin facilitates the collection of data, as well as its analysis and visualization through its user interfaces, i.e. GUIs such as screens or Mixed Reality that provide intuitive access to the data and facilitates its manipulation. Embedded in Virtual Testbeds the Digital Twin becomes an “Experimentable Digital Twin” (EDT), in which experiments can be performed and the different outcomes can be compared or evaluated. The intuitive representation of the assets allows the experts to interact with the twin, without highly detailed knowledge in computer science. The digital twin observes, records, and benchmarks experiments performed by the operator. This way the operator’s knowledge becomes digitized and thus preserved as an abstract representation of data, formulas, and models inside the digital twin. By introducing the Digital Twin into the processes carried out by different operators (not only the initially observed expert), formerly intuitive decision-making processes of the operators are enhanced based on empirical data. As a result, the Digital Twin serves as an assistance system that can guide future operators and the outcomes of the experiments become reproducible. The specific representations of interactions and outcomes also facilitate collaboration between the machine operators and other stakeholders by providing different operators a common “perspective”.

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Dokumentinformationen

Titel :

The Digital Twin as a Mediator for the Digitalization and Conservation of Expert Knowledge

Beteiligte:

Schüppstuhl, Thorsten (Herausgeber:in) / Tracht, Kirsten (Herausgeber:in) / Raatz, Annika (Herausgeber:in) / Hüsener, Dominik (Autor:in) / Schluse, Michael (Autor:in) / Kaufmann, Dorit (Autor:in) / Roßmann, Jürgen (Autor:in)

Erschienen in:

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021 ; Kapitel : 20 ; 241-251

Erscheinungsdatum :

2022-01-01

Format / Umfang :

11 pages

ISBN :

978-3-030-74032-0 , 978-3-030-74031-3

DOI :

https://doi.org/10.1007/978-3-030-74032-0_20

Medientyp :

Aufsatz/Kapitel (Buch)